design and application of a fuzzy classified rule based on learning from fuzzy examples 基于模糊示例学习的蠓虫分类规则的设计
it is a hotspot that the data mining of time serial model, classify rule, association rule in the data mining study currently 时间序列模式、分类规则和关联规则挖掘是当前数据挖掘研究中一个热点。
secondly, the thesis puts forward that conditional probability of attribute to positive example can be used to compare the information which attribute provides so as to construct decision tree and to get classify rules . and a demonstration shows that the algorithm simplifies the decision tree's building process efficiently 其次,提出了利用属性对正例的影响度来比较属性对分类提供的信息量,进而选择分类属性构造决策树的条件概率决策树算法,同时实例计算说明该算法有效地简化了决策树的生成过程。